"""simple docstring"""



from typing import Any






class  snake_case__      :


 def __init__( self    ,		lowerCamelCase						):
        __a       =    data
        __a       =    None






class  snake_case__      :


 def __init__( self						):
        __a       =    None


 def 						a__   ( self						):
        __a       =    self.head
        while temp is not None:
               print(temp.data    ,		end=" "						)
               __a       =    temp.next
        print()


 def 						a__   ( self    ,		lowerCamelCase						):
        __a       =    Node(lowerCamelCase						)
        __a       =    self.head
        __a       =    new_node






 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase						):
        if node_data_a == node_data_a:
               return
        else:
               __a       =    self.head
               while node_a is not None and node_a.data != node_data_a:
                      __a       =    node_a.next

               __a       =    self.head
               while node_a is not None and node_a.data != node_data_a:
                      __a       =    node_a.next

               if node_a is None or node_a is None:
                      return

               __a ,   __a       =    node_a.data, node_a.data


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__:Any									=			LinkedList()
   for i in range(5, 0, -1):
      ll.push(i)

   ll.print_list()

   ll.swap_nodes(1, 4)
   print("""After swapping""")
   ll.print_list()

"""simple docstring"""



from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
SCREAMING_SNAKE_CASE__:str									=			HfArgumentParser(InitializationArguments)
SCREAMING_SNAKE_CASE__:List[str]									=			parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
SCREAMING_SNAKE_CASE__:List[Any]									=			AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
SCREAMING_SNAKE_CASE__:Union[str, Any]									=			{
    """vocab_size""": len(tokenizer),
    """scale_attn_by_inverse_layer_idx""": True,
    """reorder_and_upcast_attn""": True,
}

# Load model config (GPT-2 large in this case)
SCREAMING_SNAKE_CASE__:Optional[int]									=			AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
SCREAMING_SNAKE_CASE__:Tuple									=			AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)
